2 GDS format

GDS is Genomic Data Structure, a storage format that can efficiently store genomic data and provide fast random access to subsets of the data. For more information on GDS for sequence data, read the SeqArray package vignette.

2.1 Exploring a GDS file

To use the R packages developed at the DCC for sequence data, we first need to convert a VCF file to GDS. (If the file is BCF, use https://samtools.github.io/bcftools/bcftools.html to convert to VCF.)

library(SeqArray)
data.path <- "https://github.com/UW-GAC/analysis_pipeline/raw/master/testdata"
vcffile <- "1KG_phase3_subset_chr1.vcf.gz"
if (!file.exists(vcffile)) download.file(file.path(data.path, vcffile), vcffile)
gdsfile <- "1KG_phase3_subset_chr1.gds"
seqVCF2GDS(vcffile, gdsfile, fmt.import="GT", storage.option="LZMA_RA", verbose=FALSE)

We can interact with the GDS file using the SeqArray package.

gds <- seqOpen(gdsfile)
gds
## Object of class "SeqVarGDSClass"
## File: /projects/users/stephanie/Code/TOPMed/topmed_workshop_2017/1KG_phase3_subset_chr1.gds (70.3K)
## +    [  ] *
## |--+ description   [  ] *
## |--+ sample.id   { Str8 1126 LZMA_ra(9.66%), 877B } *
## |--+ variant.id   { Int32 1120 LZMA_ra(17.5%), 793B } *
## |--+ position   { Int32 1120 LZMA_ra(78.5%), 3.4K } *
## |--+ chromosome   { Str8 1120 LZMA_ra(4.55%), 109B } *
## |--+ allele   { Str8 1120 LZMA_ra(26.0%), 1.2K } *
## |--+ genotype   [  ] *
## |  |--+ data   { Bit2 2x1126x1121 LZMA_ra(8.34%), 51.4K } *
## |  |--+ extra.index   { Int32 3x0 LZMA_ra, 18B } *
## |  \--+ extra   { Int16 0 LZMA_ra, 18B }
## |--+ phase   [  ]
## |  |--+ data   { Bit1 1126x1120 LZMA_ra(0.11%), 177B } *
## |  |--+ extra.index   { Int32 3x0 LZMA_ra, 18B } *
## |  \--+ extra   { Bit1 0 LZMA_ra, 18B }
## |--+ annotation   [  ]
## |  |--+ id   { Str8 1120 LZMA_ra(40.4%), 3.6K } *
## |  |--+ qual   { Float32 1120 LZMA_ra(2.46%), 117B } *
## |  |--+ filter   { Int32,factor 1120 LZMA_ra(2.46%), 117B } *
## |  |--+ info   [  ]
## |  \--+ format   [  ]
## \--+ sample.annotation   [  ]
# the unique sample identifier comes from the VCF header
sample.id <- seqGetData(gds, "sample.id")
length(sample.id)
## [1] 1126
head(sample.id)
## [1] "HG00096" "HG00097" "HG00099" "HG00100" "HG00101" "HG00102"
# a unique integer ID is assigned to each variant
variant.id <- seqGetData(gds, "variant.id")
length(variant.id)
## [1] 1120
head(variant.id)
## [1] 1 2 3 4 5 6
# reference allele frequency of each variant
afreq <- seqAlleleFreq(gds)
hist(afreq, breaks=50)

We can define a filter on the gds object. After using the seqSetFilter command, all subsequent reads from the gds object are restricted to the selected subset of data, until a new filter is defined or seqResetFilter is called.

seqSetFilter(gds, variant.id=1:10, sample.id=sample.id[1:5])
## # of selected samples: 5
## # of selected variants: 10

Genotype data is stored in a 3-dimensional array, where the first dimension is always 2 for diploid genotypes. The second and third dimensions are samples and variants, respectively. The values of the array denote alleles: 0 is the reference allele and 1 is the alternate allele. For multiallelic variants, other alternate alleles are represented as integers > 1.

geno <- seqGetData(gds, "genotype")
dim(geno)
## [1]  2  5 10
geno[,,1:2]
## , , 1
## 
##       sample
## allele [,1] [,2] [,3] [,4] [,5]
##   [1,]    0    0    0    0    0
##   [2,]    0    0    0    0    0
## 
## , , 2
## 
##       sample
## allele [,1] [,2] [,3] [,4] [,5]
##   [1,]    0    0    0    0    0
##   [2,]    0    0    0    0    0

The SeqVarTools package has some additional functions for interacting with SeqArray-format GDS files.

library(SeqVarTools)

# return genotypes in matrix format
getGenotype(gds)
##          variant
## sample    1     2     3     4     5     6     7     8     9     10   
##   HG00096 "0|0" "0|0" "0|0" "0|0" "0|0" "0|0" "0|0" "0|0" "0|0" "0|0"
##   HG00097 "0|0" "0|0" "0|0" "0|0" "0|0" "0|0" "0|0" "0|0" "0|0" "0|0"
##   HG00099 "0|0" "0|0" "0|0" "0|0" "0|0" "0|0" "0|0" "0|0" "0|0" "0|0"
##   HG00100 "0|0" "0|0" "0|0" "0|0" "0|0" "0|0" "0|0" "0|0" "0|0" "0|0"
##   HG00101 "0|0" "0|0" "0|0" "0|0" "0|0" "0|0" "0|0" "0|0" "0|0" "0|0"
getGenotypeAlleles(gds)
##          variant
## sample    1     2     3     4     5     6     7     8     9     10   
##   HG00096 "C|C" "C|C" "C|C" "C|C" "T|T" "G|G" "G|G" "A|A" "A|A" "C|C"
##   HG00097 "C|C" "C|C" "C|C" "C|C" "T|T" "G|G" "G|G" "A|A" "A|A" "C|C"
##   HG00099 "C|C" "C|C" "C|C" "C|C" "T|T" "G|G" "G|G" "A|A" "A|A" "C|C"
##   HG00100 "C|C" "C|C" "C|C" "C|C" "T|T" "G|G" "G|G" "A|A" "A|A" "C|C"
##   HG00101 "C|C" "C|C" "C|C" "C|C" "T|T" "G|G" "G|G" "A|A" "A|A" "C|C"
refDosage(gds)
##          variant
## sample    1 2 3 4 5 6 7 8 9 10
##   HG00096 2 2 2 2 2 2 2 2 2  2
##   HG00097 2 2 2 2 2 2 2 2 2  2
##   HG00099 2 2 2 2 2 2 2 2 2  2
##   HG00100 2 2 2 2 2 2 2 2 2  2
##   HG00101 2 2 2 2 2 2 2 2 2  2
altDosage(gds)
##          variant
## sample    1 2 3 4 5 6 7 8 9 10
##   HG00096 0 0 0 0 0 0 0 0 0  0
##   HG00097 0 0 0 0 0 0 0 0 0  0
##   HG00099 0 0 0 0 0 0 0 0 0  0
##   HG00100 0 0 0 0 0 0 0 0 0  0
##   HG00101 0 0 0 0 0 0 0 0 0  0
# look at reference and alternate alleles
refChar(gds)
##  [1] "C" "C" "C" "C" "T" "G" "G" "A" "A" "C"
altChar(gds)
##  [1] "G" "T" "T" "T" "C" "A" "A" "T" "C" "T"
# reset the filter to all variants and samples
seqResetFilter(gds)
## # of selected samples: 1,126
## # of selected variants: 1,120
# how many alleles for each variant?
n <- seqNumAllele(gds)
table(n)
## n
##    2    3    4 
## 1099   20    1
# some variants have more than one alternate allele
multi.allelic <- which(n > 2)
altChar(gds)[multi.allelic]
##  [1] "GT,G"            "G,T"             "A,T"            
##  [4] "A,T"             "ATG,ATGTG"       "C,G"            
##  [7] "A,T"             "C,T"             "A,C"            
## [10] "TAA,T"           "GTTA,GTTT"       "GCC,GCCC,G"     
## [13] "A,C"             "A,C"             "A,C"            
## [16] "CAAGCAT,CGAGCAT" "CATTATT,C"       "AT,C"           
## [19] "TGTGA,C"         "CCATT,CCATTCATT" "C,G"
# extract a particular alternate allele
# first alternate
altChar(gds, n=1)[multi.allelic]
##  [1] "GT"      "G"       "A"       "A"       "ATG"     "C"       "A"      
##  [8] "C"       "A"       "TAA"     "GTTA"    "GCC"     "A"       "A"      
## [15] "A"       "CAAGCAT" "CATTATT" "AT"      "TGTGA"   "CCATT"   "C"
# second alternate
altChar(gds, n=2)[multi.allelic]
##  [1] "G"         "T"         "T"         "T"         "ATGTG"    
##  [6] "G"         "T"         "T"         "C"         "T"        
## [11] "GTTT"      "GCCC"      "C"         "C"         "C"        
## [16] "CGAGCAT"   "C"         "C"         "C"         "CCATTCATT"
## [21] "G"
# how many variants are SNVs vs INDELs?
table(isSNV(gds, biallelic=TRUE))
## 
## FALSE  TRUE 
##   110  1010
table(isSNV(gds, biallelic=FALSE))
## 
## FALSE  TRUE 
##    99  1021
# 11 SNVs are multi-allelic

2.2 Exercises

  1. Set a filter selecting only multi-allelic variants. Inspect their genotypes using the different methods you learned above. Use the alleleDosage method to find dosage for the second (and third, etc.) alternate allele.
seqSetFilter(gds, variant.sel=multi.allelic)
## # of selected variants: 21
geno <- seqGetData(gds, "genotype")
dim(geno)
## [1]    2 1126   21
geno[,1:5,]
## , , 1
## 
##       sample
## allele [,1] [,2] [,3] [,4] [,5]
##   [1,]    1    1    0    1    1
##   [2,]    0    1    1    1    1
## 
## , , 2
## 
##       sample
## allele [,1] [,2] [,3] [,4] [,5]
##   [1,]    0    0    0    0    0
##   [2,]    0    0    0    0    0
## 
## , , 3
## 
##       sample
## allele [,1] [,2] [,3] [,4] [,5]
##   [1,]    0    0    0    0    0
##   [2,]    0    0    0    0    0
## 
## , , 4
## 
##       sample
## allele [,1] [,2] [,3] [,4] [,5]
##   [1,]    1    0    0    1    0
##   [2,]    0    0    0    0    1
## 
## , , 5
## 
##       sample
## allele [,1] [,2] [,3] [,4] [,5]
##   [1,]    0    0    0    0    0
##   [2,]    0    0    0    0    0
## 
## , , 6
## 
##       sample
## allele [,1] [,2] [,3] [,4] [,5]
##   [1,]    0    0    0    0    0
##   [2,]    0    0    0    0    0
## 
## , , 7
## 
##       sample
## allele [,1] [,2] [,3] [,4] [,5]
##   [1,]    0    0    0    0    0
##   [2,]    0    0    0    0    0
## 
## , , 8
## 
##       sample
## allele [,1] [,2] [,3] [,4] [,5]
##   [1,]    0    0    0    0    0
##   [2,]    0    0    0    0    0
## 
## , , 9
## 
##       sample
## allele [,1] [,2] [,3] [,4] [,5]
##   [1,]    0    0    0    0    2
##   [2,]    0    0    0    0    0
## 
## , , 10
## 
##       sample
## allele [,1] [,2] [,3] [,4] [,5]
##   [1,]    1    2    0    0    1
##   [2,]    0    2    0    2    1
## 
## , , 11
## 
##       sample
## allele [,1] [,2] [,3] [,4] [,5]
##   [1,]    0    0    1    0    0
##   [2,]    1    1    0    1    0
## 
## , , 12
## 
##       sample
## allele [,1] [,2] [,3] [,4] [,5]
##   [1,]    3    1    1    1    3
##   [2,]    3    3    3    1    0
## 
## , , 13
## 
##       sample
## allele [,1] [,2] [,3] [,4] [,5]
##   [1,]    0    0    0    2    0
##   [2,]    0    0    0    0    0
## 
## , , 14
## 
##       sample
## allele [,1] [,2] [,3] [,4] [,5]
##   [1,]    0    0    0    0    0
##   [2,]    0    0    0    0    0
## 
## , , 15
## 
##       sample
## allele [,1] [,2] [,3] [,4] [,5]
##   [1,]    2    0    0    0    2
##   [2,]    0    0    0    2    0
## 
## , , 16
## 
##       sample
## allele [,1] [,2] [,3] [,4] [,5]
##   [1,]    2    2    2    2    2
##   [2,]    2    2    2    2    2
## 
## , , 17
## 
##       sample
## allele [,1] [,2] [,3] [,4] [,5]
##   [1,]    2    2    2    2    2
##   [2,]    2    2    2    2    2
## 
## , , 18
## 
##       sample
## allele [,1] [,2] [,3] [,4] [,5]
##   [1,]    0    0    0    0    0
##   [2,]    0    0    0    0    0
## 
## , , 19
## 
##       sample
## allele [,1] [,2] [,3] [,4] [,5]
##   [1,]    0    0    0    0    0
##   [2,]    0    0    0    0    0
## 
## , , 20
## 
##       sample
## allele [,1] [,2] [,3] [,4] [,5]
##   [1,]    0    0    0    0    1
##   [2,]    0    0    0    0    0
## 
## , , 21
## 
##       sample
## allele [,1] [,2] [,3] [,4] [,5]
##   [1,]    0    0    0    0    0
##   [2,]    0    0    0    2    0
geno <- getGenotype(gds)
dim(geno)
## [1] 1126   21
head(geno)
##          variant
## sample    30    69    73    161   162   195   243   253   407   431  
##   HG00096 "1|0" "0|0" "0|0" "1|0" "0|0" "0|0" "0|0" "0|0" "0|0" "1|0"
##   HG00097 "1|1" "0|0" "0|0" "0|0" "0|0" "0|0" "0|0" "0|0" "0|0" "2|2"
##   HG00099 "0|1" "0|0" "0|0" "0|0" "0|0" "0|0" "0|0" "0|0" "0|0" "0|0"
##   HG00100 "1|1" "0|0" "0|0" "1|0" "0|0" "0|0" "0|0" "0|0" "0|0" "0|2"
##   HG00101 "1|1" "0|0" "0|0" "0|1" "0|0" "0|0" "0|0" "0|0" "2|0" "1|1"
##   HG00102 "0|1" "0|0" "0|0" "1|0" "0|0" "0|0" "0|0" "0|0" "0|0" "2|2"
##          variant
## sample    434   610   627   645   689   756   765   814   988   1014 
##   HG00096 "0|1" "3|3" "0|0" "0|0" "2|0" "2|2" "2|2" "0|0" "0|0" "0|0"
##   HG00097 "0|1" "1|3" "0|0" "0|0" "0|0" "2|2" "2|2" "0|0" "0|0" "0|0"
##   HG00099 "1|0" "1|3" "0|0" "0|0" "0|0" "2|2" "2|2" "0|0" "0|0" "0|0"
##   HG00100 "0|1" "1|1" "2|0" "0|0" "0|2" "2|2" "2|2" "0|0" "0|0" "0|0"
##   HG00101 "0|0" "3|0" "0|0" "0|0" "2|0" "2|2" "2|2" "0|0" "0|0" "1|0"
##   HG00102 "0|1" "3|3" "0|2" "0|0" "0|0" "2|2" "1|2" "0|0" "0|0" "0|1"
##          variant
## sample    1056 
##   HG00096 "0|0"
##   HG00097 "0|0"
##   HG00099 "0|0"
##   HG00100 "0|2"
##   HG00101 "0|0"
##   HG00102 "0|2"
geno <- getGenotypeAlleles(gds)
head(geno)
##          variant
## sample    30       69    73    161   162   195   243   253   407  
##   HG00096 "GT|GTT" "A|A" "G|G" "A|G" "A|A" "A|A" "C|C" "G|G" "T|T"
##   HG00097 "GT|GT"  "A|A" "G|G" "G|G" "A|A" "A|A" "C|C" "G|G" "T|T"
##   HG00099 "GTT|GT" "A|A" "G|G" "G|G" "A|A" "A|A" "C|C" "G|G" "T|T"
##   HG00100 "GT|GT"  "A|A" "G|G" "A|G" "A|A" "A|A" "C|C" "G|G" "T|T"
##   HG00101 "GT|GT"  "A|A" "G|G" "G|A" "A|A" "A|A" "C|C" "G|G" "C|T"
##   HG00102 "GTT|GT" "A|A" "G|G" "A|G" "A|A" "A|A" "C|C" "G|G" "T|T"
##          variant
## sample    431       434      610       627   645   689   756              
##   HG00096 "TAA|TA"  "G|GTTA" "G|G"     "G|G" "G|G" "C|G" "CGAGCAT|CGAGCAT"
##   HG00097 "T|T"     "G|GTTA" "GCC|G"   "G|G" "G|G" "G|G" "CGAGCAT|CGAGCAT"
##   HG00099 "TA|TA"   "GTTA|G" "GCC|G"   "G|G" "G|G" "G|G" "CGAGCAT|CGAGCAT"
##   HG00100 "TA|T"    "G|GTTA" "GCC|GCC" "C|G" "G|G" "G|C" "CGAGCAT|CGAGCAT"
##   HG00101 "TAA|TAA" "G|G"    "G|GC"    "G|G" "G|G" "C|G" "CGAGCAT|CGAGCAT"
##   HG00102 "T|T"     "G|GTTA" "G|G"     "G|C" "G|G" "G|G" "CGAGCAT|CGAGCAT"
##          variant
## sample    765         814     988           1014      1056 
##   HG00096 "C|C"       "CT|CT" "CGTGA|CGTGA" "C|C"     "A|A"
##   HG00097 "C|C"       "CT|CT" "CGTGA|CGTGA" "C|C"     "A|A"
##   HG00099 "C|C"       "CT|CT" "CGTGA|CGTGA" "C|C"     "A|A"
##   HG00100 "C|C"       "CT|CT" "CGTGA|CGTGA" "C|C"     "A|G"
##   HG00101 "C|C"       "CT|CT" "CGTGA|CGTGA" "CCATT|C" "A|A"
##   HG00102 "CATTATT|C" "CT|CT" "CGTGA|CGTGA" "C|CCATT" "A|G"
dos <- refDosage(gds)
head(dos)
##          variant
## sample    30 69 73 161 162 195 243 253 407 431 434 610 627 645 689 756 765
##   HG00096  1  2  2   1   2   2   2   2   2   1   1   0   2   2   1   0   0
##   HG00097  0  2  2   2   2   2   2   2   2   0   1   0   2   2   2   0   0
##   HG00099  1  2  2   2   2   2   2   2   2   2   1   0   2   2   2   0   0
##   HG00100  0  2  2   1   2   2   2   2   2   1   1   0   1   2   1   0   0
##   HG00101  0  2  2   1   2   2   2   2   1   0   2   1   2   2   1   0   0
##   HG00102  1  2  2   1   2   2   2   2   2   0   1   0   1   2   2   0   0
##          variant
## sample    814 988 1014 1056
##   HG00096   2   2    2    2
##   HG00097   2   2    2    2
##   HG00099   2   2    2    2
##   HG00100   2   2    2    1
##   HG00101   2   2    1    2
##   HG00102   2   2    1    1
dos <- altDosage(gds)
head(dos)
##          variant
## sample    30 69 73 161 162 195 243 253 407 431 434 610 627 645 689 756 765
##   HG00096  1  0  0   1   0   0   0   0   0   1   1   2   0   0   1   2   2
##   HG00097  2  0  0   0   0   0   0   0   0   2   1   2   0   0   0   2   2
##   HG00099  1  0  0   0   0   0   0   0   0   0   1   2   0   0   0   2   2
##   HG00100  2  0  0   1   0   0   0   0   0   1   1   2   1   0   1   2   2
##   HG00101  2  0  0   1   0   0   0   0   1   2   0   1   0   0   1   2   2
##   HG00102  1  0  0   1   0   0   0   0   0   2   1   2   1   0   0   2   2
##          variant
## sample    814 988 1014 1056
##   HG00096   0   0    0    0
##   HG00097   0   0    0    0
##   HG00099   0   0    0    0
##   HG00100   0   0    0    1
##   HG00101   0   0    1    0
##   HG00102   0   0    1    1
dos <- alleleDosage(gds, n=2)
head(dos)
##          variant
## sample    30 69 73 161 162 195 243 253 407 431 434 610 627 645 689 756 765
##   HG00096  0  0  0   0   0   0   0   0   0   0   0   0   0   0   1   2   2
##   HG00097  0  0  0   0   0   0   0   0   0   2   0   0   0   0   0   2   2
##   HG00099  0  0  0   0   0   0   0   0   0   0   0   0   0   0   0   2   2
##   HG00100  0  0  0   0   0   0   0   0   0   1   0   0   1   0   1   2   2
##   HG00101  0  0  0   0   0   0   0   0   1   0   0   0   0   0   1   2   2
##   HG00102  0  0  0   0   0   0   0   0   0   2   0   0   1   0   0   2   1
##          variant
## sample    814 988 1014 1056
##   HG00096   0   0    0    0
##   HG00097   0   0    0    0
##   HG00099   0   0    0    0
##   HG00100   0   0    0    1
##   HG00101   0   0    0    0
##   HG00102   0   0    0    1
dos <- alleleDosage(gds, n=3)
head(dos)
##          variant
## sample    30 69 73 161 162 195 243 253 407 431 434 610 627 645 689 756 765
##   HG00096  0  0  0   0   0   0   0   0   0   0   0   2   0   0   0   0   0
##   HG00097  0  0  0   0   0   0   0   0   0   0   0   1   0   0   0   0   0
##   HG00099  0  0  0   0   0   0   0   0   0   0   0   1   0   0   0   0   0
##   HG00100  0  0  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##   HG00101  0  0  0   0   0   0   0   0   0   0   0   1   0   0   0   0   0
##   HG00102  0  0  0   0   0   0   0   0   0   0   0   2   0   0   0   0   0
##          variant
## sample    814 988 1014 1056
##   HG00096   0   0    0    0
##   HG00097   0   0    0    0
##   HG00099   0   0    0    0
##   HG00100   0   0    0    0
##   HG00101   0   0    0    0
##   HG00102   0   0    0    0
  1. Use the hwe function in SeqVarTools to run a Hardy-Weinberg Equilibrium test on each variant. Identify a variant with low p-value and inspect its genotypes. (Note that the HWE test is only valid for biallelic variants.)
seqResetFilter(gds)
## # of selected samples: 1,126
## # of selected variants: 1,120
hwe.res <- hwe(gds)
lowp <- !is.na(hwe.res$p) & hwe.res$p < 1e-4
head(hwe.res[lowp,])
##     variant.id nAA nAa naa     afreq            p         f
## 75          75 702 336  88 0.7726465 1.070663e-06 0.1506466
## 92          92 632 381 113 0.7304618 3.558878e-06 0.1407120
## 98          98 672 335 119 0.7455595 2.369695e-12 0.2158342
## 105        105  93 272 761 0.2033748 7.851777e-16 0.2544970
## 114        114 299 482 345 0.4795737 1.745346e-06 0.1424409
## 150        150 471 447 208 0.6167851 8.020208e-08 0.1602251
seqSetFilter(gds, variant.id=75)
## # of selected variants: 1
table(getGenotype(gds))
## 
## 0|0 0|1 1|0 1|1 
## 702 165 171  88
table(refDosage(gds))
## 
##   0   1   2 
##  88 336 702
seqClose(gds)